What the Benchmark Card Leaves Blank: Values That Would Falsify an Agent Claim

The reader's question, and why a chart is not an answer

A reader asked: the benchmark card lists required fields but no values, so which specific values would let me falsify an AI trading agent's claim? It matters because a claim you can check beats a chart you must trust. A return chart is a single scalar produced by many interacting parts, so on its own it cannot show whether an apparent result came from the agent, the market regime, data leakage, an execution shortcut, or the surrounding software. That attribution problem is the reason the supplied framework asks for more than the number.

What the source actually says, and where it stops

Observation: the supplied source is an evaluation framework, not a report of investment performance. It asks an evaluator to define exactly what system is being tested, reconstruct what information the system could see at each decision, and charge realistic execution costs; it then asks for comparison against relevant baselines, repetition of the run, and preservation of the trace from instruction to outcome. It also states explicitly that passing the checklist makes a result easier to interpret and reproduce, while profitability, safety, and deployment suitability remain separate questions.

Observation: the card as supplied reports no filled values. Interpretation: that single fact is what keeps the claim unverifiable. Explanation (one mechanism): falsification requires a stated value that a contrary observation could contradict; an empty field admits every outcome, so nothing can count as disconfirming evidence. Hypothetical: if the same headline return could have been produced by a leaked input or a favorable regime, the chart would look identical either way — which is presumably why the framework demands more than the number.

Missing-components checklist: one value per field

The card names fields but supplies no values, so every line below is hypothetical — a field plus the value that would make a claim falsifiable.

Agent boundary (value required): which components count as the agent versus the harness, so a result cannot be credited to surrounding software. Decision-time inputs (value required): the information set genuinely visible at each decision, so look-ahead or leakage can be ruled out. Cost assumption (value required): the fees, spread, slippage, and fill assumptions charged, so an execution shortcut becomes visible. Baseline set (value required): which relevant comparison systems ran under the same conditions. Repeat count (value required): how many runs were performed, so regime luck can be told apart from repeatable behavior. Preserved trace (value required): an artifact from instruction to outcome, so a reviewer can reconstruct the path rather than infer it from a summary.

Any line left at 'value required' marks a specific place where the claim is currently unfalsifiable. That is the practical answer to the reader's question: the values are the falsifiers, and they are absent.

Why reproducibility outlives the software that produced it

Observation: related supplied material notes that a trading system returning a different answer after a harmless restart has a research problem as well as an operations problem, and that when models, strategies, and evaluations change over time, an append-only evidence trail helps separate genuine prospective learning from a history rewritten after outcomes are known. Explanation: a preserved trace is what lets a third party confirm that a decision existed before its outcome. Hypothetical: if restart handling silently rewrote or duplicated the record, a reproducibility pass could look cleaner than the process that produced it.

Observation: the framework states that passing the checklist does not establish profitability, safety, or deployment suitability. So the honest status of the claim under discussion is checklist, not result. Treating an unfilled checklist as validation would repeat exactly the error the framework warns about.

How to use this without overclaiming

For readers evaluating automated trading and AI systems, the transferable move is to ask for values, not verdicts. Interpretation: three questions do most of the work — what exactly was tested, what could it see, and what did it cost? If those answers are missing, uncertainty does not shrink because the method sounds rigorous; it stays exactly where the empty fields left it.

Limitation: nothing here reports performance, and no filled values were supplied, so this reading cannot tell anyone whether any particular agent works. It only identifies which blank values would let someone else try to find out.